Abstract
Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and control for the types of methodological and inferential errors. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition.
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Notes
I focus on fMRI because it is the most commonly used neuroimaging method, but parallel analyses can be made about other neuroimaging methods, e.g. MEG or fNIRS, mutatis mutandis.
The literature on the epistemology of neuroimaging includes a considerable number of works by philosophers of science and cognitive scientists. Naturally, a comprehensive review of this literature is beyond the scope of this paper.
Klein (2010) provides a concise review of similar works by cognitive scientists who disagree on the epistemic value of neuroimaging.
The term ‘productive theory-ladenness’ is not necessarily novel; for example, Chang (2012) uses it in his discussion of Hanson. However, in that context, theories are productive in the sense that they give “intelligibility to observations” by providing a conceptual framework and auxiliary assumptions (p. 89). I use the term in a different, more methodological sense in which the knowledge and control of potential errors are productive in establishing the reliability of findings. It is also important to note that what makes this kind of theory-ladenness productive is not interdisciplinarity per se but precisely (a) and (b) above.
Of course, in an fMRI experiment, theories of magnetic resonance and hemodynamics are complemented with computational and statistical techniques (e.g. Fourier transforms, spatial and temporal filtering, multivoxel pattern analysis, etc.) to preprocess, model and analyze data, which also constitute part of the productive theory-ladenness described here.
Duhem's distinction between theory-ladenness in physics and physiology will be revisited.
Having proper error probabilities between 0 and 1, as formulated in Mayo’s error-statistical account would be a more effective way of controlling for potential errors, for details see Mayo (1996) and Mayo and Spanos (2011). These error probabilities can be calculated in a series of studies similar to those in which false positive and false negative rates of diagnostic tests are calculated.
Though on the basis of different physical knowledge, productive theory-ladenness occurs in PET, too. Indeed, it should be clear by now that productive theory-ladenness occurs in any successful use of a complex instrument of measurement or observation.
Nonetheless, it is still the case that experimental knowledge coming from behavioral experiments in the cognitive psychology of human memory played a major role in designing the tasks used in neuroimaging experiments. Thus, the question arises; ‘to what extent does experimental knowledge from cognitive psychology provide background knowledge for neuroimaging experiments?’ This question is related to issues of cognitive ontology, which I plan to address elsewhere but it is not directly relevant to my purposes here.
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Aktunc, M.E. Productive theory-ladenness in fMRI. Synthese 198, 7987–8003 (2021). https://doi.org/10.1007/s11229-019-02125-9
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DOI: https://doi.org/10.1007/s11229-019-02125-9